Path Forecast Evaluation

A path forecast refers to the sequence of forecasts 1 to H periods into the future. A summary of the range of possible paths the predicted variable may follow for a given confidence level requires construction of simultaneous confidence regions that adjust for any covariance between the elements of the path forecast. This paper shows how to construct such regions with the joint predictive density and Scheffé’s (1953) S-method. In addition, the joint predictive density can be used to construct simple statistics to evaluate the local internal consistency of a forecasting exercise of a system of variables. Monte Carlo simulations demonstrate that these simultaneous confidence regions provide approximately correct coverage in situations where traditional error bands, based on the collection of marginal predictive densities for each horizon, are vastly off mark. The paper showcases these methods with an application to the most recent monetary episode of interest rate hikes in the U.S. macroeconomy.path forecast, simultaneous confidence region, error bands

The announcement effect: evidence from open market desk data

Paper for a conference sponsored by the Federal Reserve Bank of New York entitled Financial Innovation and Monetary TransmissionOpen market operations ; Monetary policy ; Federal Open Market Committee ; Federal funds market (United States)

Estimation and Inference by the Method of Projection Minimum Distance

A covariance-stationary vector of variables has a Wold representation whose coefficients can be semi-parametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of minimum distance to estimate model parameters. We call this estimator projection minimum distance (PMD) and show that its parameter estimates are consistent and asymptotically normal. In many cases, PMD is asymptotically equivalent to maximum likelihood estimation (MLE) and nests GMM as a special case. In fact, models whose ML estimation would require numerical routines (such as VARMA models) can often be estimated by simple least-squares routines and almost as efficiently by PMD. Because PMD imposes no constraints on the dynamics of the system, it is often consistent in many situations where alternative estimators would be inconsistent.We provide several Monte Carlo experiments and an empirical application in support of the new techniques introduced.Econometric and statistical methods

Measuring systematic monetary policy

Monetary policy

A chronology of turning points in economic activity: Spain 1850-2011

This paper codifies in a systematic and transparent way a historical chronology of business cycle turning points for Spain reaching back to 1850 at annual frequency, and 1939 at monthly frequency. Such an exercise would be incomplete without assessing the new chronology itself and against others —this we do with modern statistical tools of signal detection theory. We also use these tools to determine which of several existing economic activity indexes provide a better signal on the underlying state of the economy. We conclude by evaluating candidate leading indicators and hence construct recession probability forecasts up to 12 months in the future.Business cycles ; Spain

Collaborative music interaction on tabletops: an HCI approach

With the advent of tabletop interaction, collaborative activities are better supported than they are on single-user PCs because there exists a physical shareable space, and interaction with digital data is more embodied and social. In sound and music computing, collaborative music making has traditionally been done using interconnected networks, but using separated computers. Musical tabletops introduce opportunities of playing in collaboration through sharing physically the same musical interface. However, few tabletop musical interfaces exploit this collaborative potential (e.g. the Reactable). We are interested in looking into how collaboration can be fully supported by means of musical tabletops for music performance in contrast with more traditional settings. We are also looking at whether collective musical engagement can be enhanced by providing more suitable interfaces to collaboration. In HCI and software development, we find an iterative process approach of design and evaluation—where evaluation allows us to identify key issues that can be addressed in the next design iteration of the system. Using a similar iterative approach, we plan to design and evaluate some tabletop musical interfaces. The aim is to understand what design choices can enhance and enrich collaboration and collective musical engagement on these systems. In this paper, we explain the evaluation methodologies we have undertaken in three preliminary pilot studies, and the lessons we have learned. Initial findings indicate that evaluating tabletop musical interfaces is a complex endeavour which requires an approach as close as possible to a real context, with an interdisciplinary approach provided by interaction analysis techniques

Empirical simultaneous confidence regions for path-forecasts

Measuring and displaying uncertainty around path-forecasts, i.e. forecasts made in period T about the expected trajectory of a random variable in periods T+1 to T+H is a key ingredient for decision making under uncertainty. The probabilistic assessment about the set of possible trajectories that the variable may follow over time is summarized by the simultaneous confidence region generated from its forecast generating distribution. However, if the null model is only approximative or altogether unavailable, one cannot derive analytic expressions for this confidence region, and its non-parametric estimation is impractical given commonly available predictive sample sizes. Instead, this paper derives the approximate rectangular confidence regions that control false discovery rate error, which are a function of the predictive sample covariance matrix and the empirical distribution of the Mahalanobis distance of the path-forecast errors. These rectangular regions are simple to construct and appear to work well in a variety of cases explored empirically and by simulation. The proposed techniques are applied to provide confidence bands around the Fed and Bank of England real-time path-forecasts of growth and inflation. --Path forecast,forecast uncertainty,simultaneous confidence region,Scheffé's S-method,Mahalanobis distance,false discovery rate

Multi-touch interaction principles for collaborative real-time music activities: towards a pattern language

In this paper we give an analysis of the literature on a set of problems that can arise when undertaking the interaction design of multi-touch applications for collaborative real-time music activities, which are designed for multitouch technologies (e.g. smartphones, tablets, interactive tabletops, among others). Each problem is described, and a candidate design pattern (CDP) is suggested in the form of a short sentence and a diagram—an approach inspired by Christopher Alexander’s A Pattern Language. These solutions relate to the fundamental collaborative principles of democratic relationships, identities and collective interplay. We believe that this approach might disseminate forms of best design practice for collaborative music applications, in order to produce real-time musical systems which are collaborative and expressive

Empirical Simultaneous Confidence Regions for Path-Forecasts

Measuring and displaying uncertainty around path-forecasts, i.e. forecasts made in period T about the expected trajectory of a random variable in periods T+1 to T+H is a key ingredient for decision making under uncertainty. The probabilistic assessment about the set of possible trajectories that the variable may follow over time is summarized by the simultaneous confidence region generated from its forecast generating distribution. However, if the null model is only approximative or altogether unavailable, one cannot derive analytic expressions for this confidence region, and its non-parametric estimation is impractical given commonly available predictive sample sizes. Instead, this paper derives the approximate rectangular confidence regions that control false discovery rate error, which are a function of the predictive sample covariance matrix and the empirical distribution of the Mahalanobis distance of the path-forecast errors. These rectangular regions are simple to construct and appear to work well in a variety of cases explored empirically and by simulation. The proposed techniques are applied to provide con.dence bands around the Fed and Bank of England real-time path-forecasts of growth and inflation.path forecast, forecast uncertainty, simultaneous confidence region, Scheffé’s S-method,Mahalanobis distance, false discovery rate.